



'''simple docstring'''






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCamelCase__:  List[str]				      =     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 					snake_case_	(   _lowerCAmelCase    :		Tuple    ) ->  List[str]:

							if isinstance(a__  , torch.Tensor    ):
														return image
							elif isinstance(a__  , PIL.Image.Image    ):
														UpperCAmelCase					:			Tuple									=						[image]

							UpperCAmelCase					:			List[Any]									=						[trans(img.convert('''RGB'''    )    ) for img in image]
							UpperCAmelCase					:			Optional[int]									=						torch.stack(a__    )
							return image




class 							SCREAMING_SNAKE_CASE(				A__     ):







	"""simple docstring"""






	def __init__(		self     :	int						,     __snake_case     :	Union[str, Any]						,     __snake_case     :	Any						)     ->						Any:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								UpperCAmelCase					:			Tuple									=						DDIMScheduler.from_config(scheduler.config						)

								self.register_modules(unet=lowercase_						,     scheduler=lowercase_						)







	def 	A      (		self     :	str						,     __snake_case     :	Optional[int]						)     ->						Any:







								if strength < 0 or strength > 1:
															raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""						)







	def 	A      (		self     :	Optional[Any]						,     __snake_case     :	Dict						,     __snake_case     :	Optional[Any]						,     __snake_case     :	List[Any]						)     ->						List[Any]:
								UpperCAmelCase					:			Dict									=						min(int(num_inference_steps * strength						)						,     lowercase_						)

								UpperCAmelCase					:			Union[str, Any]									=						max(num_inference_steps - init_timestep						,     0						)
								UpperCAmelCase					:			Dict									=						self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start







	def 	A      (		self     :	List[str]						,     __snake_case     :	str						,     __snake_case     :	Dict						,     __snake_case     :	Union[str, Any]						,     __snake_case     :	int						,     __snake_case     :	int						,     __snake_case     :	Union[str, Any]=None						)     ->						Optional[Any]:







								if not isinstance(lowercase_						,     (torch.Tensor, PIL.Image.Image, list)						):
															raise ValueError(
															    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_						)}"""						)

								UpperCAmelCase					:			Tuple									=						image.to(device=lowercase_						,     dtype=lowercase_						)

								if isinstance(lowercase_						,     lowercase_						) and len(lowercase_						) != batch_size:
															raise ValueError(
															    F"""You have passed a list of generators of length {len(lowercase_						)}, but requested an effective batch"""
															    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

								UpperCAmelCase					:			Dict									=						init_latents.shape
								UpperCAmelCase					:			Tuple									=						randn_tensor(lowercase_						,     generator=lowercase_						,     device=lowercase_						,     dtype=lowercase_						)

								# get latents
								print('''add noise to latents at timestep'''						,     lowercase_						)
								UpperCAmelCase					:			str									=						self.scheduler.add_noise(lowercase_						,     lowercase_						,     lowercase_						)
								UpperCAmelCase					:			List[str]									=						init_latents

								return latents







	@torch.no_grad()
	def __call__(		self     :	int						,     __snake_case     :	Union[torch.FloatTensor, PIL.Image.Image] = None						,     __snake_case     :	float = 0.8						,     __snake_case     :	int = 1						,     __snake_case     :	Optional[Union[torch.Generator, List[torch.Generator]]] = None						,     __snake_case     :	float = 0.0						,     __snake_case     :	int = 50						,     __snake_case     :	Optional[bool] = None						,     __snake_case     :	Optional[str] = "pil"						,     __snake_case     :	bool = True						,     )     ->						Union[ImagePipelineOutput, Tuple]:
								self.check_inputs(lowercase_						)

								# 2. Preprocess image
								UpperCAmelCase					:			Any									=						preprocess(lowercase_						)

								# 3. set timesteps
								self.scheduler.set_timesteps(lowercase_						,     device=self.device						)
								UpperCAmelCase				,						UpperCAmelCase					:			List[str]									=						self.get_timesteps(lowercase_						,     lowercase_						,     self.device						)
								UpperCAmelCase					:			Optional[Any]									=						timesteps[:1].repeat(lowercase_						)

								# 4. Prepare latent variables
								UpperCAmelCase					:			List[str]									=						self.prepare_latents(lowercase_						,     lowercase_						,     lowercase_						,     self.unet.dtype						,     self.device						,     lowercase_						)
								UpperCAmelCase					:			Union[str, Any]									=						latents

								# 5. Denoising loop
								for t in self.progress_bar(lowercase_						):
															# 1. predict noise model_output
															UpperCAmelCase					:			str									=						self.unet(lowercase_						,     lowercase_						).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															UpperCAmelCase					:			Union[str, Any]									=						self.scheduler.step(
															    lowercase_						,     lowercase_						,     lowercase_						,     eta=lowercase_						,     use_clipped_model_output=lowercase_						,     generator=lowercase_						,     ).prev_sample

								UpperCAmelCase					:			int									=						(image / 2 + 0.5).clamp(0						,     1						)
								UpperCAmelCase					:			Optional[int]									=						image.cpu().permute(0						,     2						,     3						,     1						).numpy()
								if output_type == "pil":
															UpperCAmelCase					:			List[Any]									=						self.numpy_to_pil(lowercase_						)

								if not return_dict:
															return (image, latent_timestep.item())

								return ImagePipelineOutput(images=lowercase_						)






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCamelCase__  =					{
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts )
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config )
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights )

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False
  elif args.student_type == "gpt2":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[str]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Training" )
  parser.add_argument("--force"    ,     action="store_true"    ,     help="Overwrite dump_path if it already exists." )

  parser.add_argument(
      "--dump_path"    ,     type=a__    ,     required=a__    ,     help="The output directory (log, checkpoints, parameters, etc.)" )
  parser.add_argument(
      "--data_file"    ,     type=a__    ,     required=a__    ,     help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."    ,     )

  parser.add_argument(
      "--student_type"    ,     type=a__    ,     choices=["distilbert", "roberta", "gpt2"]    ,     required=a__    ,     help="The student type (DistilBERT, RoBERTa)."    ,     )
  parser.add_argument("--student_config"    ,     type=a__    ,     required=a__    ,     help="Path to the student configuration." )
  parser.add_argument(
      "--student_pretrained_weights"    ,     default=a__    ,     type=a__    ,     help="Load student initialization checkpoint." )

  parser.add_argument(
      "--teacher_type"    ,     choices=["bert", "roberta", "gpt2"]    ,     required=a__    ,     help="Teacher type (BERT, RoBERTa)." )
  parser.add_argument("--teacher_name"    ,     type=a__    ,     required=a__    ,     help="The teacher model." )

  parser.add_argument("--temperature"    ,     default=2.0    ,     type=a__    ,     help="Temperature for the softmax temperature." )
  parser.add_argument(
      "--alpha_ce"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the distillation loss. Must be >=0." )
  parser.add_argument(
      "--alpha_mlm"    ,     default=0.0    ,     type=a__    ,     help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."    ,     )
  parser.add_argument("--alpha_clm"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the CLM loss. Must be >=0." )
  parser.add_argument("--alpha_mse"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the MSE loss. Must be >=0." )
  parser.add_argument(
      "--alpha_cos"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the cosine embedding loss. Must be >=0." )

  parser.add_argument(
      "--mlm"    ,     action="store_true"    ,     help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
  parser.add_argument(
      "--mlm_mask_prop"    ,     default=0.15    ,     type=a__    ,     help="Proportion of tokens for which we need to make a prediction."    ,     )
  parser.add_argument("--word_mask"    ,     default=0.8    ,     type=a__    ,     help="Proportion of tokens to mask out." )
  parser.add_argument("--word_keep"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to keep." )
  parser.add_argument("--word_rand"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to randomly replace." )
  parser.add_argument(
      "--mlm_smoothing"    ,     default=0.7    ,     type=a__    ,     help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."    ,     )
  parser.add_argument("--token_counts"    ,     type=a__    ,     help="The token counts in the data_file for MLM." )

  parser.add_argument(
      "--restrict_ce_to_mask"    ,     action="store_true"    ,     help="If true, compute the distillation loss only the [MLM] prediction distribution."    ,     )
  parser.add_argument(
      "--freeze_pos_embs"    ,     action="store_true"    ,     help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."    ,     )
  parser.add_argument(
      "--freeze_token_type_embds"    ,     action="store_true"    ,     help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."    ,     )

  parser.add_argument("--n_epoch"    ,     type=a__    ,     default=3    ,     help="Number of pass on the whole dataset." )
  parser.add_argument("--batch_size"    ,     type=a__    ,     default=5    ,     help="Batch size (for each process)." )
  parser.add_argument(
      "--group_by_size"    ,     action="store_false"    ,     help="If true, group sequences that have similar length into the same batch. Default is true."    ,     )

  parser.add_argument(
      "--gradient_accumulation_steps"    ,     type=a__    ,     default=50    ,     help="Gradient accumulation for larger training batches."    ,     )
  parser.add_argument("--warmup_prop"    ,     default=0.05    ,     type=a__    ,     help="Linear warmup proportion." )
  parser.add_argument("--weight_decay"    ,     default=0.0    ,     type=a__    ,     help="Weight decay if we apply some." )
  parser.add_argument("--learning_rate"    ,     default=5e-4    ,     type=a__    ,     help="The initial learning rate for Adam." )
  parser.add_argument("--adam_epsilon"    ,     default=1e-6    ,     type=a__    ,     help="Epsilon for Adam optimizer." )
  parser.add_argument("--max_grad_norm"    ,     default=5.0    ,     type=a__    ,     help="Max gradient norm." )
  parser.add_argument("--initializer_range"    ,     default=0.02    ,     type=a__    ,     help="Random initialization range." )

  parser.add_argument(
      "--fp16"    ,     action="store_true"    ,     help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"    ,     )
  parser.add_argument(
      "--fp16_opt_level"    ,     type=a__    ,     default="O1"    ,     help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )    ,     )
  parser.add_argument("--n_gpu"    ,     type=a__    ,     default=1    ,     help="Number of GPUs in the node." )
  parser.add_argument("--local_rank"    ,     type=a__    ,     default=-1    ,     help="Distributed training - Local rank" )
  parser.add_argument("--seed"    ,     type=a__    ,     default=56    ,     help="Random seed" )

  parser.add_argument("--log_interval"    ,     type=a__    ,     default=500    ,     help="Tensorboard logging interval." )
  parser.add_argument("--checkpoint_interval"    ,     type=a__    ,     default=4_000    ,     help="Checkpoint interval." )
  _UpperCamelCase           =    parser.parse_args()
  sanity_checks(a__ )

  # ARGS #
  init_gpu_params(a__ )
  set_seed(a__ )
  if args.is_master:
    if os.path.exists(args.dump_path ):
      if not args.force:
        raise ValueError(
            f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
            " itUse `--force` if you want to overwrite it" )
      else:
        shutil.rmtree(args.dump_path )

    if not os.path.exists(args.dump_path ):
      os.makedirs(args.dump_path )
    logger.info(f'Experiment will be dumped and logged in {args.dump_path}' )

    # SAVE PARAMS #
    logger.info(f'Param: {args}' )
    with open(os.path.join(args.dump_path    ,     "parameters.json" )    ,     "w" ) as f:
      json.dump(vars(a__ )    ,     a__    ,     indent=4 )
    git_log(args.dump_path )

  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.student_type]
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  _UpperCamelCase           =    teacher_tokenizer_class.from_pretrained(args.teacher_name )
  _UpperCamelCase           =    {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    _UpperCamelCase           =    tokenizer.all_special_tokens.index(a__ )
    _UpperCamelCase           =    tokenizer.all_special_ids[idx]
  logger.info(f'Special tokens {special_tok_ids}' )
  _UpperCamelCase           =    special_tok_ids
  _UpperCamelCase           =    tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(f'Loading data from {args.data_file}' )
  with open(args.data_file    ,     "rb" ) as fp:
    _UpperCamelCase           =    pickle.load(a__ )

  if args.mlm:
    logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)' )
    with open(args.token_counts    ,     "rb" ) as fp:
      _UpperCamelCase           =    pickle.load(a__ )

    _UpperCamelCase           =    np.maximum(a__    ,     1 ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      _UpperCamelCase           =    0.0  # do not predict special tokens
    _UpperCamelCase           =    torch.from_numpy(a__ )
  else:
    _UpperCamelCase           =    None

  _UpperCamelCase           =    LmSeqsDataset(params=a__    ,     data=a__ )
  logger.info("Data loader created." )

  # STUDENT #
  logger.info(f'Loading student config from {args.student_config}' )
  _UpperCamelCase           =    student_config_class.from_pretrained(args.student_config )
  _UpperCamelCase           =    True

  if args.student_pretrained_weights is not None:
    logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}' )
    _UpperCamelCase           =    student_model_class.from_pretrained(args.student_pretrained_weights    ,     config=a__ )
  else:
    _UpperCamelCase           =    student_model_class(a__ )

  if args.n_gpu > 0:
    student.to(f'cuda:{args.local_rank}' )
  logger.info("Student loaded." )

  # TEACHER #
  _UpperCamelCase           =    teacher_model_class.from_pretrained(args.teacher_name    ,     output_hidden_states=a__ )
  if args.n_gpu > 0:
    teacher.to(f'cuda:{args.local_rank}' )
  logger.info(f'Teacher loaded from {args.teacher_name}.' )

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(a__    ,     a__ )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(a__    ,     a__ )

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0 ) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  _UpperCamelCase           =    Distiller(
      params=a__    ,     dataset=a__    ,     token_probs=a__    ,     student=a__    ,     teacher=a__ )
  distiller.train()
  logger.info("Let's go get some drinks." )


if __name__ == "__main__":
   main()




